{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ldm\\garbage_128_conditional_ldm_0\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from yldiffusers import conditional_garbage_ldm_config_dict, get_config, LDMConfig\n",
    "config = get_config(conditional_garbage_ldm_config_dict, LDMConfig)\n",
    "print(config.output_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "🚀yl-diffusion-LDM training starts!\n"
     ]
    },
    {
     "data": {
      "text/plain": "HBox(children=(HTML(value='train : Epoch [1/4]'), FloatProgress(value=0.0, max=11847.0), HTML(value='')))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2a33eaccd7ba407499c8e69311c38b9f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 4.00 GiB total capacity; 2.22 GiB already allocated; 1.70 MiB free; 2.24 GiB reserved in total by PyTorch)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mRuntimeError\u001B[0m                              Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-2-2bec7b68e5b2>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[0myldiffusers\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mLDM\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[0mLDM\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mLDM\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 3\u001B[1;33m \u001B[0mLDM\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mtrain\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;32mD:\\毕业论文\\xzc\\diffusion-pytorch\\yldiffusers\\models\\models.py\u001B[0m in \u001B[0;36mtrain\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    321\u001B[0m                         \u001B[0mnoise\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtorch\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mrandn\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlatent_z\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mto\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlatent_z\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdevice\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    322\u001B[0m                         \u001B[0mnoisy_latent_zt\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mnoise_scheduler\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0madd_noise\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mlatent_z\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnoise\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtimesteps\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 323\u001B[1;33m                         \u001B[0mnoise_pred\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmodel\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnoisy_latent_zt\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtimesteps\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mclass_labels\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0mlabels\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0msample\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    324\u001B[0m                         \u001B[0mnoise_loss\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mloss_f\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnoise_pred\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnoise\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    325\u001B[0m                         \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0maccelerator\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbackward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mnoise_loss\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[0;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[1;32m-> 1051\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1052\u001B[0m         \u001B[1;31m# Do not call functions when jit is used\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\accelerate\\utils\\operations.py\u001B[0m in \u001B[0;36m__call__\u001B[1;34m(self, *args, **kwargs)\u001B[0m\n\u001B[0;32m    488\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    489\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m__call__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 490\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mconvert_to_fp32\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmodel_forward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    491\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    492\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0m__getstate__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\torch\\cuda\\amp\\autocast_mode.py\u001B[0m in \u001B[0;36mdecorate_autocast\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    139\u001B[0m         \u001B[1;32mdef\u001B[0m \u001B[0mdecorate_autocast\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    140\u001B[0m             \u001B[1;32mwith\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 141\u001B[1;33m                 \u001B[1;32mreturn\u001B[0m \u001B[0mfunc\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0margs\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    142\u001B[0m         \u001B[1;32mreturn\u001B[0m \u001B[0mdecorate_autocast\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    143\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\毕业论文\\xzc\\diffusion-pytorch\\yldiffusers\\models\\unet.py\u001B[0m in \u001B[0;36mforward\u001B[1;34m(self, sample, timestep, class_labels, return_dict)\u001B[0m\n\u001B[0;32m    265\u001B[0m                 )\n\u001B[0;32m    266\u001B[0m             \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 267\u001B[1;33m                 \u001B[0msample\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mres_samples\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mdownsample_block\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mhidden_states\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0msample\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtemb\u001B[0m\u001B[1;33m=\u001B[0m\u001B[0memb\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    268\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    269\u001B[0m             \u001B[0mdown_block_res_samples\u001B[0m \u001B[1;33m+=\u001B[0m \u001B[0mres_samples\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[0;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[1;32m-> 1051\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1052\u001B[0m         \u001B[1;31m# Do not call functions when jit is used\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\diffusers\\models\\unet_2d_blocks.py\u001B[0m in \u001B[0;36mforward\u001B[1;34m(self, hidden_states, temb)\u001B[0m\n\u001B[0;32m    870\u001B[0m                 \u001B[0mhidden_states\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mtorch\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mutils\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mcheckpoint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mcreate_custom_forward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mresnet\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mhidden_states\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtemb\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    871\u001B[0m             \u001B[1;32melse\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 872\u001B[1;33m                 \u001B[0mhidden_states\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mresnet\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mhidden_states\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mtemb\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    873\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    874\u001B[0m             \u001B[0moutput_states\u001B[0m \u001B[1;33m+=\u001B[0m \u001B[1;33m(\u001B[0m\u001B[0mhidden_states\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[0;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[1;32m-> 1051\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1052\u001B[0m         \u001B[1;31m# Do not call functions when jit is used\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\diffusers\\models\\resnet.py\u001B[0m in \u001B[0;36mforward\u001B[1;34m(self, input_tensor, temb)\u001B[0m\n\u001B[0;32m    486\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    487\u001B[0m         \u001B[0mhidden_states\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mdropout\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mhidden_states\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 488\u001B[1;33m         \u001B[0mhidden_states\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconv2\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mhidden_states\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    489\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    490\u001B[0m         \u001B[1;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mconv_shortcut\u001B[0m \u001B[1;32mis\u001B[0m \u001B[1;32mnot\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001B[0m in \u001B[0;36m_call_impl\u001B[1;34m(self, *input, **kwargs)\u001B[0m\n\u001B[0;32m   1049\u001B[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001B[0;32m   1050\u001B[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001B[1;32m-> 1051\u001B[1;33m             \u001B[1;32mreturn\u001B[0m \u001B[0mforward_call\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m*\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m**\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m   1052\u001B[0m         \u001B[1;31m# Do not call functions when jit is used\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m   1053\u001B[0m         \u001B[0mfull_backward_hooks\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mnon_full_backward_hooks\u001B[0m \u001B[1;33m=\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;33m[\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001B[0m in \u001B[0;36mforward\u001B[1;34m(self, input)\u001B[0m\n\u001B[0;32m    441\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    442\u001B[0m     \u001B[1;32mdef\u001B[0m \u001B[0mforward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mself\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0minput\u001B[0m\u001B[1;33m:\u001B[0m \u001B[0mTensor\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;33m->\u001B[0m \u001B[0mTensor\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 443\u001B[1;33m         \u001B[1;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0m_conv_forward\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0minput\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mweight\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mbias\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m    444\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    445\u001B[0m \u001B[1;32mclass\u001B[0m \u001B[0mConv3d\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0m_ConvNd\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;32mD:\\ana\\lib\\site-packages\\torch\\nn\\modules\\conv.py\u001B[0m in \u001B[0;36m_conv_forward\u001B[1;34m(self, input, weight, bias)\u001B[0m\n\u001B[0;32m    437\u001B[0m                             \u001B[0mweight\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mbias\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mself\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mstride\u001B[0m\u001B[1;33m,\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m    438\u001B[0m                             _pair(0), self.dilation, self.groups)\n\u001B[1;32m--> 439\u001B[1;33m         return F.conv2d(input, weight, bias, self.stride,\n\u001B[0m\u001B[0;32m    440\u001B[0m                         self.padding, self.dilation, self.groups)\n\u001B[0;32m    441\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mRuntimeError\u001B[0m: CUDA out of memory. Tried to allocate 2.00 MiB (GPU 0; 4.00 GiB total capacity; 2.22 GiB already allocated; 1.70 MiB free; 2.24 GiB reserved in total by PyTorch)"
     ]
    }
   ],
   "source": [
    "from yldiffusers import LDM\n",
    "LDM = LDM(config)\n",
    "LDM.train()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import glob\n",
    "from PIL import Image\n",
    "sample_images = sorted(glob.glob(f\"{config.output_dir}/samples/*.png\"))\n",
    "Image.open(sample_images[-1])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'config'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[1;32m<ipython-input-2-37cbcd399eb3>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mpickle\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m      2\u001B[0m \u001B[1;32mwith\u001B[0m \u001B[0mopen\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34m'conditional_ldm/oxford-102-flowers_ldm_128/conditional_ldm_config.pickle'\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m'rb'\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mf\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 3\u001B[1;33m     \u001B[0mconfig\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mpickle\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mload\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mf\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      4\u001B[0m     \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mconfig\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'config'"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "with open('conditional_ldm/oxford-102-flowers_ldm_128/conditional_ldm_config.pickle', 'rb') as f:\n",
    "    config = pickle.load(f)\n",
    "    print(config)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "data": {
      "text/plain": "\u001B[31m╭─\u001B[0m\u001B[31m──────────────────────────────\u001B[0m\u001B[31m \u001B[0m\u001B[1;31mTraceback \u001B[0m\u001B[1;2;31m(most recent call last)\u001B[0m\u001B[31m \u001B[0m\u001B[31m───────────────────────────────\u001B[0m\u001B[31m─╮\u001B[0m\n\u001B[31m│\u001B[0m in \u001B[92m<module>\u001B[0m                                                                                      \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m                                                                                                  \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m \u001B[33mD:\\pytorch\\ylcv\\diffusion-pytorch\\yldiffusers\\utils\\__init__.py\u001B[0m:\u001B[94m87\u001B[0m in \u001B[92mload_pickle_file\u001B[0m           \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m                                                                                                  \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m84 \u001B[0m\u001B[2m│   \u001B[0mtorch.backends.cudnn.deterministic = \u001B[94mTrue\u001B[0m  \u001B[2m# 固定网络结构\u001B[0m                               \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m85 \u001B[0m\u001B[94mdef\u001B[0m \u001B[92mload_pickle_file\u001B[0m(pickle_file_path):                                                     \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m86 \u001B[0m\u001B[2m│   \u001B[0m\u001B[94mwith\u001B[0m \u001B[96mopen\u001B[0m(pickle_file_path, \u001B[33m'\u001B[0m\u001B[33mrb\u001B[0m\u001B[33m'\u001B[0m) \u001B[94mas\u001B[0m f:                                                 \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m \u001B[31m❱ \u001B[0m87 \u001B[2m│   │   \u001B[0mconfig = pickle.load(f)                                                             \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m88 \u001B[0m\u001B[2m│   \u001B[0m\u001B[94mreturn\u001B[0m config                                                                           \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m89 \u001B[0m                                                                                            \u001B[31m│\u001B[0m\n\u001B[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001B[0m\n\u001B[1;91mModuleNotFoundError: \u001B[0mNo module named \u001B[32m'config'\u001B[0m\n",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">D:\\pytorch\\ylcv\\diffusion-pytorch\\yldiffusers\\utils\\__init__.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">87</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">load_pickle_file</span>           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">84 │   </span>torch.backends.cudnn.deterministic = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span>  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"># 固定网络结构</span>                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">85 </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">load_pickle_file</span>(pickle_file_path):                                                     <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">86 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">with</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">open</span>(pickle_file_path, <span style=\"color: #808000; text-decoration-color: #808000\">'rb'</span>) <span style=\"color: #0000ff; text-decoration-color: #0000ff\">as</span> f:                                                 <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>87 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span>config = pickle.load(f)                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">88 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">return</span> config                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">89 </span>                                                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">ModuleNotFoundError: </span>No module named <span style=\"color: #008000; text-decoration-color: #008000\">'config'</span>\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yldiffusers import load_pickle_file\n",
    "config = load_pickle_file('ldm/oxford-102-flowers_128_conditional_ldm_0/conditional_ldm_config.pickle')\n",
    "config"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "\u001B[31m╭─\u001B[0m\u001B[31m──────────────────────────────\u001B[0m\u001B[31m \u001B[0m\u001B[1;31mTraceback \u001B[0m\u001B[1;2;31m(most recent call last)\u001B[0m\u001B[31m \u001B[0m\u001B[31m───────────────────────────────\u001B[0m\u001B[31m─╮\u001B[0m\n\u001B[31m│\u001B[0m in \u001B[92m<module>\u001B[0m                                                                                      \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m                                                                                                  \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m \u001B[33mD:\\pytorch\\ylcv\\diffusion-pytorch\\yldiffusers\\models\\models.py\u001B[0m:\u001B[94m274\u001B[0m in \u001B[92m__init__\u001B[0m                   \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m                                                                                                  \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m271 \u001B[0m\u001B[94mclass\u001B[0m \u001B[4;92mLDM\u001B[0m(DDPM):                                                                           \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m272 \u001B[0m\u001B[2m│   \u001B[0m\u001B[94mdef\u001B[0m \u001B[92m__init__\u001B[0m(\u001B[96mself\u001B[0m, config=\u001B[94mNone\u001B[0m):                                                       \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m273 \u001B[0m\u001B[2m│   │   \u001B[0m\u001B[96msuper\u001B[0m(LDM, \u001B[96mself\u001B[0m).\u001B[92m__init__\u001B[0m()                                                        \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m \u001B[31m❱ \u001B[0m274 \u001B[2m│   │   \u001B[0m\u001B[96mself\u001B[0m.init_base(config)                                                             \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m275 \u001B[0m\u001B[2m│   │   \u001B[0m\u001B[96mself\u001B[0m.init_dataloader(config)                                                       \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m276 \u001B[0m\u001B[2m│   │   \u001B[0m\u001B[96mself\u001B[0m.init_model(config)                                                            \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m277 \u001B[0m\u001B[2m│   \u001B[0m\u001B[94mdef\u001B[0m \u001B[92minit_model\u001B[0m(\u001B[96mself\u001B[0m, config):                                                          \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m                                                                                                  \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m \u001B[33mD:\\pytorch\\ylcv\\diffusion-pytorch\\yldiffusers\\models\\models.py\u001B[0m:\u001B[94m26\u001B[0m in \u001B[92minit_base\u001B[0m                   \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m                                                                                                  \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m 23 \u001B[0m\u001B[2m│   │   │   \u001B[0m\u001B[96mself\u001B[0m.init_model(config)                                                        \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m 24 \u001B[0m\u001B[2m│   \u001B[0m\u001B[94mdef\u001B[0m \u001B[92minit_base\u001B[0m(\u001B[96mself\u001B[0m, config):                                                           \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m 25 \u001B[0m\u001B[2m│   │   \u001B[0m\u001B[96mself\u001B[0m.config = config                                                               \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m \u001B[31m❱ \u001B[0m 26 \u001B[2m│   │   \u001B[0m\u001B[96mself\u001B[0m.condition = \u001B[94mTrue\u001B[0m \u001B[94mif\u001B[0m \u001B[96mself\u001B[0m.config.task_type.split(\u001B[33m'\u001B[0m\u001B[33m_\u001B[0m\u001B[33m'\u001B[0m)[\u001B[94m0\u001B[0m] == \u001B[33m'\u001B[0m\u001B[33mconditional\u001B[0m\u001B[33m'\u001B[0m \u001B[94mel\u001B[0m   \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m 27 \u001B[0m\u001B[2m│   │   \u001B[0m\u001B[96mself\u001B[0m.logger = \u001B[96mdict\u001B[0m(                                                                \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m 28 \u001B[0m\u001B[2m│   │   │   \u001B[0mtrain_noise_loss=\u001B[94m0\u001B[0m, train_total_num = \u001B[94m0\u001B[0m, train_noise_loss_list = []            \u001B[31m│\u001B[0m\n\u001B[31m│\u001B[0m   \u001B[2m 29 \u001B[0m\u001B[2m│   │   \u001B[0m)                                                                                  \u001B[31m│\u001B[0m\n\u001B[31m╰──────────────────────────────────────────────────────────────────────────────────────────────────╯\u001B[0m\n\u001B[1;91mAttributeError: \u001B[0m\u001B[32m'NoneType'\u001B[0m object has no attribute \u001B[32m'task_type'\u001B[0m\n",
      "text/html": "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000\">╭─────────────────────────────── </span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Traceback </span><span style=\"color: #bf7f7f; text-decoration-color: #bf7f7f; font-weight: bold\">(most recent call last)</span><span style=\"color: #800000; text-decoration-color: #800000\"> ────────────────────────────────╮</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">&lt;module&gt;</span>                                                                                      <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">D:\\pytorch\\ylcv\\diffusion-pytorch\\yldiffusers\\models\\models.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">274</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__init__</span>                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">271 </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">class</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00; text-decoration: underline\">LDM</span>(DDPM):                                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">272 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">__init__</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, config=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">None</span>):                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">273 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">super</span>(LDM, <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>).<span style=\"color: #00ff00; text-decoration-color: #00ff00\">__init__</span>()                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span>274 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.init_base(config)                                                             <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">275 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.init_dataloader(config)                                                       <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">276 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.init_model(config)                                                            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">277 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">init_model</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, config):                                                          <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #808000; text-decoration-color: #808000\">D:\\pytorch\\ylcv\\diffusion-pytorch\\yldiffusers\\models\\models.py</span>:<span style=\"color: #0000ff; text-decoration-color: #0000ff\">26</span> in <span style=\"color: #00ff00; text-decoration-color: #00ff00\">init_base</span>                   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>                                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 23 │   │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.init_model(config)                                                        <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 24 │   </span><span style=\"color: #0000ff; text-decoration-color: #0000ff\">def</span> <span style=\"color: #00ff00; text-decoration-color: #00ff00\">init_base</span>(<span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>, config):                                                           <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 25 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.config = config                                                               <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span> <span style=\"color: #800000; text-decoration-color: #800000\">❱ </span> 26 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">│   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.condition = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">True</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">if</span> <span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.config.task_type.split(<span style=\"color: #808000; text-decoration-color: #808000\">'_'</span>)[<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>] == <span style=\"color: #808000; text-decoration-color: #808000\">'conditional'</span> <span style=\"color: #0000ff; text-decoration-color: #0000ff\">el</span>   <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 27 │   │   </span><span style=\"color: #00ffff; text-decoration-color: #00ffff\">self</span>.logger = <span style=\"color: #00ffff; text-decoration-color: #00ffff\">dict</span>(                                                                <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 28 │   │   │   </span>train_noise_loss=<span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>, train_total_num = <span style=\"color: #0000ff; text-decoration-color: #0000ff\">0</span>, train_noise_loss_list = []            <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">│</span>   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\"> 29 │   │   </span>)                                                                                  <span style=\"color: #800000; text-decoration-color: #800000\">│</span>\n<span style=\"color: #800000; text-decoration-color: #800000\">╰──────────────────────────────────────────────────────────────────────────────────────────────────╯</span>\n<span style=\"color: #ff0000; text-decoration-color: #ff0000; font-weight: bold\">AttributeError: </span><span style=\"color: #008000; text-decoration-color: #008000\">'NoneType'</span> object has no attribute <span style=\"color: #008000; text-decoration-color: #008000\">'task_type'</span>\n</pre>\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yldiffusers import LDM\n",
    "LDM = LDM()\n",
    "LDM.load('ldm/oxford-102-flowers_128_conditional_ldm_0/conditional_ldm_pipeline.pickle', 'ldm/oxford-102-flowers_128_conditional_ldm_0/conditional_ldm_config.pickle')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=200.0), HTML(value='')))",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "1a76bd26ba614e81be260f41c5661553"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "LDM.generate(label_index=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}